arXiv:2602.19172v2 Announce Type: replace Abstract: Realizable online regression can behave very differently from online classification. Even without any margin or stochastic assumptions, realizability may enforce horizon-free (finite) cumulative loss under metric-like losses, even when the analogous classification problem has an infinite mistake bound. We study realizable online regression in the adversarial model under losses that satisfy an approximate triangle inequality (approximate pseudo-metrics). Recent work of Attias et al. shows that the minimax realizable cumulative loss is characte
This is a technical research paper from arXiv, representing ongoing academic work in the field of machine learning theory.
For a strategic reader, this specific publication has minimal direct importance as it concerns fundamental theoretical advancements in AI rather than immediate applications or market shifts.
This paper does not immediately change current applications, market dynamics, or strategic outlooks; it contributes to the foundational understanding of online regression.
Further theoretical understanding of online regression models.
Potential minor contributions to the design principles of future machine learning algorithms.
No discernible third-order consequences outside of academic AI research.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG